How much is too much? Leveraging ads audience estimation to evaluate public profile uniqueness

Terence Chen, Abdelberi Chaabane, Pierre Ugo Tournoux, Mohamed Ali Kaafar, Roksana Boreli

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

8 Citations (Scopus)

Abstract

This paper addresses the important goal of quantifying the threat of linking external records to public Online Social Networks (OSN) user profiles, by providing a method to estimate the uniqueness of such profiles and by studying the amount of information carried by public profile attributes. Our first contribution is to leverage the Ads audience estimation platform of a major OSN to compute the information surprisal (IS) based uniqueness of public profiles, independently from the used profiles dataset. Then, we measure the quantity of information carried by the revealed attributes and evaluate the impact of the public release of selected combinations of these attributes on the potential to identify user profiles. Our measurement results, based on an unbiased sample of more than 400 thousand Facebook public profiles, show that, when disclosed in such profiles, current city has the highest individual attribute potential for unique identification and the combination of gender, current city and age can identify close to 55% of users to within a group of 20 and uniquely identify around 18% of users. We envisage the use of our methodology to assist both OSNs in designing better anonymization strategies when releasing user records and users to evaluate the potential for external parties to uniquely identify their public profiles and hence make it easier to link them with other data sources.

Original languageEnglish
Title of host publicationPrivacy Enhancing Technologies
Subtitle of host publication13th International Symposium, PETS 2013 : proceedings
EditorsEmiliano De Cristofaro, Matthew Wright
Place of PublicationBerlin
PublisherSpringer, Springer Nature
Pages225-244
Number of pages20
ISBN (Electronic)9783642390777
ISBN (Print)9783642390760
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event13th International Symposium on Privacy Enhancing Technologies, PETS 2013 - Bloomington, IN, United States
Duration: 10 Jul 201312 Jul 2013

Publication series

NameLecture Notes in Computer Science
Volume7981
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Symposium on Privacy Enhancing Technologies, PETS 2013
CountryUnited States
CityBloomington, IN
Period10/07/1312/07/13

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